GCP Cloud Run & MongoDB Atlas Automation Pipeline

Welcome to the comprehensive automation solution for managing MongoDB Atlas database users dynamically based on Google Cloud Platform (GCP) Cloud Run resources.

📋 Project Overview & Use Cases

This project provides a cross-platform Terraform and Python-based pipeline to automatically generate and manage MongoDB Atlas database users with secure, randomly generated passwords. It is divided into two main modules depending on your infrastructure architecture:

1. Cloud Run Jobs (Cloud-Run-Job/)

Use Case: Ideal for batch processes, scheduled tasks, and asynchronous workers deployed as Cloud Run Jobs.

• Discovers GCP Cloud Run Jobs containing a specific environment name.
• Creates matching database users in MongoDB Atlas.
• Generates secure 20-character passwords and assigns appropriate database read/write roles.

2. Cloud Run Services (Cloud-Run-Service/)

Use Case: Ideal for web applications, APIs, and microservices functioning as continuous Cloud Run Services.

• Discovers active GCP Cloud Run Services filtered by environment.
• Provisions corresponding MongoDB Atlas database users dynamically.
• Automatically handles the integration and outputs secure connection URIs for application use.

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📁 Directory Structure

cloudrun-mongo-cred-rotator/
├── Cloud-Run-Job/         # Terraform configs and scripts for Cloud Run Jobs
│   ├── scripts/deploy.py  # Automation Python deployment script
│   ├── scripts/get_jobs.py # Python script to fetch GCP Cloud Run jobs
│   ├── main.tf            # Atlas user provisioning declarations
│   └── README.md          # Detailed Job-specific module documentation
│
└── Cloud-Run-Service/     # Terraform configs and scripts for Cloud Run Services
    ├── scripts/deploy.py  # Automation Python deployment script
    ├── scripts/get_users.py # Python script to fetch GCP Cloud Run services
    ├── main.tf            # Atlas user provisioning declarations
    └── README.md          # Detailed Service-specific module documentation

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🚀 Instructions to Follow

Prerequisites

1. Terraform (v1.0+)
2. Google Cloud SDK (gcloud CLI) installed and authenticated.
3. Python 3 installed and available on your PATH.
4. A MongoDB Atlas Account with a configured Project and Cluster.

Setup & Deployment

1. Clone the Repository: Start by cloning this project to your local machine:

   git clone https://github.com/mpandey95/cloudrun-mongo-cred-rotator.git
   cd cloudrun-mongo-cred-rotator

2. Choose Your Target Resource Module: Determine if you are managing users for Cloud Run Jobs or Cloud Run Services, and navigate to the respective directory:

   For Cloud Run Jobs:

   cd Cloud-Run-Job/

   For Cloud Run Services:

   cd Cloud-Run-Service/

3. Configure Your Credentials: Create an environment-specific variable file (e.g., dev.tfvars, prod.tfvars) inside the chosen module directory:

   gcp_project_id      = "your-gcp-project-id"
   gcp_state_bucket    = "your-tf-state-bucket"
   atlas_project_id    = "your-atlas-project-id"
   atlas_public_key    = "your-atlas-public-key"
   atlas_private_key   = "your-atlas-private-key"
   atlas_cluster_name  = "your-cluster-name"

4. Deploy the Infrastructure: Run the platform-agnostic Python deployment wrapper script, passing your target environment (e.g., dev, uat, prod): For Cloud Run Jobs:

   # Ensure you are in the Cloud-Run-Job/ directory
   chmod +x scripts/deploy.py
   ./scripts/deploy.py --env="dev" 

   For Cloud Run Services:

   # Ensure you are in the Cloud-Run-Service/ directory
   chmod +x scripts/deploy.py
   ./scripts/deploy.py --env="dev" 

   (To destroy existing infrastructure, append --destroy to the corresponding script).

5. Retrieve Connection Details: Once successfully applied, Terraform will automatically persist the newly generated database credentials locally in connection_strings.json and a list of created accounts in users.txt.

Note: For detailed module architecture, diagrams, and specific troubleshooting, please refer to the individual README.md files located within the Cloud-Run-Job/ and Cloud-Run-Service/ directories.

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👨‍💻 Author & Skills

Manish Pandey — Senior DevOps/Platform Engineer

🛠️ Technology Stack

• ☁️ Cloud & Platforms: GCP, AWS
• ⚙️ Platform & DevOps: Kubernetes, Docker, Terraform, Helm, Ansible, CI/CD
• 🔐 Security & Ops: IAM, Networking, Monitoring, Secrets Management
• 🧑‍💻 Programming: Python, Bash, YAML
• 💾 Database: SQL, MongoDB

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🤝 Connect With Me

• GitHub: mpandey95
• LinkedIn: manish-pandey95
• Email: mnshkmrpnd@gmail.com

📄 License

See LICENSE

Support: GitHub • LinkedIn